class_docstr: Test MLlib algorithms on plain lists, to make sure they're passed through
  as NumPy arrays.
code: def test_gmm_deterministic(self):
      from pyspark.mllib.clustering import GaussianMixture
  
      x = range(0, 100, 10)
      y = range(0, 100, 10)
      data = self.sc.parallelize([[a, b] for a, b in zip(x, y)])
      clusters1 = GaussianMixture.train(
          data, 5, convergenceTol=0.001, maxIterations=10, seed=63
      )
      clusters2 = GaussianMixture.train(
          data, 5, convergenceTol=0.001, maxIterations=10, seed=63
      )
      for c1, c2 in zip(clusters1.weights, clusters2.weights):
          self.assertEqual(round(c1, 7), round(c2, 7))
  
imports: import os
  import tempfile
  from shutil import rmtree
  import unittest
  from numpy import array, array_equal
  from py4j.protocol import Py4JJavaError
  from pyspark.mllib.fpm import FPGrowth
  from pyspark.mllib.recommendation import Rating
  from pyspark.mllib.regression import LabeledPoint
  from pyspark.serializers import CPickleSerializer
  from pyspark.testing.mllibutils import MLlibTestCase
  from pyspark.testing.utils import eventually
label_desc: Use image node_id 5 for calling the ListTests obj's underlying member method code with example usage: obj.test_gmm_deterministic() without return types